Lenny's Podcast: Product | Career | Growth
00:05:10 · Anish Acharya, Lenny Rachitsky
It's auto catalytic effects, which just means you're using the technology to improve your process, but it's not truly recursive. So, I think everything from the most empirical to the most technical view points in the other direction, and yet we can't seem to let go of this fantasy. [00:05:26] S0: Something I've been thinking about recently, even seeing these crazy stories about OpenAI's models hacking Hugging Face, all these stories to me feels like there's always been this question of are we on the fast takeoff or the slow takeoff scenario? And it feels very much so that we are on the slow takeoff scenario, because every one of these milestones it's like, "Holy shit, it hacked ..." We had no idea it was doing this, but we're catching it, we're watching it, we're observing it, we're iterating, evolving. There's always this fear, "Okay, but tomorrow it's going to take off." What I'm hearing from you is that's probably not the case, which I think is the source of a lot of people's fears, this idea that all of a sudden it's going to become super, super intelligent and then we're in big trouble.
00:06:05 · Anish Acharya
[00:06:05] S1: That's right. The line of reasoning for that case is always everything up until now, then something happens that no one can quite articulate and then fast take off. So, I don't believe that that's going to happen. I do think that model progress is happening faster than ever before, but if you look at something like economic diffusion, I grew up in a small town, I went back home last summer, people's lives haven't changed that much. So if nothing else, the sort of slow rate of economic diffusion will catch it. I think the other thing that's under discussed, Lenny, is how many problems are truly intelligence bound? If you had a data center of PhDs working at FedEx or Domino's Pizza, are they going to be exponentially dominating supply chain and pizzas? I don't think so. So, I think we might be overestimating how many problems are intelligence bound versus bound by other things. I mean, I have so many thoughts on this. I don't think we give the average employee enough credit. I think we have this abstraction of a white collar employee. The white collar manager, the abstraction is like some Dilbert-esque manager who's just shuffling paper all day long. We have this abstraction of consumers that they're sort of these low agency NPCs. Of course, that would never apply to us or our friends. We have this abstraction that everybody else's job is super automatable by AI, but of course ours is not. So, I think when you actually get into the details, a lot of people are actually excited to better themselves, get more leverage. And you see this with, of course, sophisticated companies like Google, but even a company like Kavak where they sell used cars in Mexico, they've got this concept of a Jedi academy where they're teaching everybody at the company, including the mechanics, how to use the new tools and technologies and at the end of the six-week course they ship a cutting edge in-production agent. So, I actually think that more people are embracing the technology than we sort of like to discuss. I think a big change is going to be using AI versus reorganizing your entire company around AI. And a great example of this is if you look at the diffusion of electricity as a technology, it took 40 years for us to get from the inception of electricity to reorganizing factories.
00:09:24 · Anish Acharya, Lenny Rachitsky
And that means burning the buildings down and starting from scratch versus taking what was previously coal and simply swapping it with electricity. So, I do think that the most ambitious companies are rethinking everything around the models, and those that are a little less ambitious or perhaps a little earlier are thinking more about how do we give people in existing orgs, existing job functions, access to the technology. [00:10:01] S0: So, kind of a theme I'm hearing so far is we can be a little less stressed about where things are going and the future of your job, your careers.
00:10:01 · Lenny Rachitsky, Anish Acharya
[00:10:01] S0: So, kind of a theme I'm hearing so far is we can be a little less stressed about where things are going and the future of your job, your careers. [00:10:10] S1: I think so, man. I mean, I think if you even just think of the kind of incentives for the CEO and executives, Sundar running Google, he doesn't want to run a more efficient $4 trillion company, he wants to build a $40 trillion company. So anytime you have an economically productive unit, it's rational to, especially if it gets more productive, to maintain that sort of presence in your organization. And then, I don't know what you hear, but anecdotally, I talked to a good friend who's an executive at Google and I said, "Hey, have you laid anyone off?" And he said, "No, we didn't. What we instead do is now rip through our roadmap." So, two years of roadmap happens in three months, and we're actually our hardest problem is knowing what to add to the roadmap, which by the way is every PM's fantasy. How much emotion has been drained on prioritization conversations between you and I? So yeah, I actually think that people shouldn't be as stressed and I think they should feel really empowered. And by the way, the best way to do it is just to ship stuff. I mean, Claire is my muse. She's so awesome because she's- [00:11:07] S0: Claire Vo?
00:11:07 · Lenny Rachitsky, Anish Acharya
[00:11:07] S0: Claire Vo? [00:11:07] S1: ... always shipping. Yes, Claire Vo, she's shipping, she's trying things, she is not afraid to be a little embarrassed by it. And if you just see her whole kind of affect, she feels like the best version of herself that she's ever been and I think we all have an opportunity to be that. [00:11:23] S0: I love that. I want to be Claire Vo when I grew up. [00:11:25] S1: Totally. [00:11:26] S0: Kind of along those lines, you have this interesting take that company building more and more is going to become this kind of series of creating loops and creating series of loops. Talk about that.
00:11:36 · Anish Acharya
[00:11:36] S1: Yeah, yeah. Well, I think the broad concept and the loops concept kind of gets teased a little on X because at sometimes it feels like maybe we're big braining it. I know there was a big meme around graphs, like the next stage of loops is graphs, but let me make the kind of steel man for it, which is we had prompts and then we invented agents. Agents of course are just models in a loop with tools and memory and skill files. And then we had sort of loops, which are sets of agents that are doing tasks. If you look at a lot of work in coding, coding is such a great domain because you have the best models and you have the most sort of technically apt customer, plus you have these established loops, like bug fix, or a bug report comes in, repro gets generated, bug fix gets created, bug fix gets reviewed. If high risk, then humans should confirm that it's okay to ship to prod. And if low risk, it just gets shipped. And maybe you even email the customer and say, "Hey, we fixed the bug you reported." That happens in five minutes. There are many loops like that in engineering, everything from bug fixes to customer feedback, to sales demos, to new feature development. So, coding sets itself up well. So, you have a coding loop and the kind of change it makes is to the code base. My question is what are the business loops? So if you're the GM of a business, you're looking across many job functions and you've got loops running in coding, and marketing, and sales, and support, and legal, the output of all of those loops is something that is itself a loop that you should be able to optimize for. And I think the strong form of this is that it sends a message to the CEO saying, "Hey, we need to actually make a change to one of the physical aspects of the business, or to our business model, or to our strategy." So, I think we're going to see this sort of cascading set of everything from a loop per person, loop per job function, loop across entire business units to loops that can run large parts of the company. With that said, I think humans are a critical ingredient. I just don't think that most work in the organization can be done fully autonomously. When you think of what a human will do in this AI native company, sales, support, strategy and exceptions. And all those things are super critical.